With the widespread development of cloud computing, the dramatic increase in the number and size of data centers (DCs) has resulted in substantial energy consumption and serious environmental problems. This creates a challenge for the further development of DCs. It is imperative to improve the overall energy efficiency of DCs. According to the statistics, servers and cooling systems are the main energy-consuming components of DCs. Recently, several energy-efficient strategies have been developed to address these problems. However, most of these works only consider the energy optimization of servers or cooling systems separately. Therefore, a Joint Energy Efficiency Optimization Scheme (JEES) is proposed in this paper, where the energy consumed by servers and the cooling system is jointly considered, and coordinately optimized. JEES includes a dynamic online task scheduling algorithm based on marginal cost evaluation, a resource management strategy that integrates the workload prediction technique to manage resources, and a task migration method using marginal cost evaluation. By using the proposed techniques, the total energy consumption of DCs can be reduced. Extensive experiments have been conducted based on real-world workload traces, and the results demonstrate that compared with other techniques, the proposed scheme effectively improves the overall resource utilization and reduces the total energy consumption of DCs.

随着云计算的广泛发展，数据中心（DC）的数量和规模急剧增加，导致大量能源消耗和严重的环境问题。这对 DC 的进一步发展提出了挑战。提高数据中心的整体能源效率势在必行。据统计，服务器和冷却系统是数据中心的主要耗能部件。最近，已经开发了几种节能策略来解决这些问题。然而，这些工作大多只单独考虑服务器或冷却系统的能源优化。因此，乔INT ê NERGY è fficiency优化小号cheme (JEES) 在本文中被提出，其中服务器和冷却系统消耗的能量被联合考虑，并协调优化。JEES 包括基于边际成本评估的动态在线任务调度算法、集成工作负载预测技术来管理资源的资源管理策略以及使用边际成本评估的任务迁移方法。通过使用所提出的技术，可以降低 DC 的总能耗。基于真实世界的工作负载轨迹进行了大量实验，结果表明，与其他技术相比，所提出的方案有效地提高了整体资源利用率并降低了 DC 的总能耗。